04.1_targeted-logFC
zagor
2024-11-05
1 libraries
library(scales)
library(ggplot2)
# packageVersion("ggplot2")
library(magrittr)
# library(crayon)
library(rstatix)
# library(ggpubr)
# library(Compositional)
# library(equalCovs)
# library(psych)
library(corrplot)
# library(pdist)
library(RColorBrewer)
library(plotly)
library(heatmaply)
# library(Hmisc)
library(htmlwidgets)
library(viridis)
library(ggh4x)
`%nin%` = Negate(`%in%`)2 f-ctions
3 predefined contrasts
4 metabolomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'metabolomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)4.1 palette
n = 6
# my.ggplot.palette = brewer.pal(8, "Set2")[c(8, 2, 6, 4, 5, 3)]#[c(8, 2, 6, 7, 5, 3)]
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
pie(rep(1, length(my.ggplot.palette)),
col = my.ggplot.palette,
main = 'ggplot palette',
labels = c('C', 'H', 'D', 'HD',
# 'HDW',
'W'))
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
# 'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)
par(mfrow = c(2,2))
nn = 9
# (intervals = cut(c(-1,1), n*2))
intervals = cut(c(-1,1),
breaks = setdiff(seq(-1, 1, 0.1), 0),
include.lowest = TRUE)
my.heatmap.col1 = c(rev(brewer.pal(nn, 'Reds')), 'white', brewer.pal(nn, 'Blues'))
# my.heatmap.col1 = cividis(19, direction = -1)
pie(rep(1, length(my.heatmap.col1)),
col = rev(my.heatmap.col1),
labels = levels(intervals),
main = 'heatmap palette 1')
my.heatmap.col2 = c(my.heatmap.col1[1:3],
rep('grey90', length(4:(nn*2-2))),
my.heatmap.col1[(nn*2-1): (nn*2 + 1)])
pie(rep(1, length(my.heatmap.col1)),
col = rev(my.heatmap.col2),
labels = levels(intervals),
main = 'heatmap palette 2')
par(mfrow = c(1,1))
4.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
# https://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
4.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
# useful in case of more genotypes
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'metabolomics')
4.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
# X11();
# plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 3# # try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# # try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'metabolomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'metabolomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
openxlsx::write.xlsx(Desiree.stat, '../reports/Desiree.logFC-metabolomics_leaves.xlsx', asTable = TRUE)4.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-metabolomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'metabolomics',
width = 6, height = 6, cnt = cnt)5 hormonomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'hormonomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)5.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)5.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
5.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'hormonomics')
5.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
# X11();
# plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 11# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') my.pheatmap(df, mytitle, mycond, subdir = 'hormonomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'hormonomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'hormonomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
openxlsx::write.xlsx(Desiree.stat, '../reports/Desiree.logFC-hormonomics_leaves.xlsx', asTable = TRUE)5.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-hormonomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'hormonomics',
width = 6, height = 6, cnt = cnt)6 transcriptomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'transcriptomics-leaves')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
omics = omics[omics$PostInductionDay %in% 1:14, ]
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics = omics[-which(omics$Condition == 'W' & omics$PostInductionDay %in% c(8,14)), ]
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 1 7 8 14
## C 4 4 4 4
## H 4 4 4 4
## D 0 0 4 4
## HD 0 0 4 4
## W 4 4 0 0mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)6.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)6.2 density
# column in which measurements start
nx = 6
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
6.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
lims = data.long.SE %>%
dplyr::group_by(molecule) %>%
dplyr::summarise(Panel = molecule,
ymin=min(mean-se),ymax=max(mean+se),
n = 5
)
lims = unique(lims)
lims = split(lims, lims$Panel) # make a list
lims = lapply(lims, function(l) {
scale_y_continuous(limits = c(l$ymin, l$ymax),
n.breaks = l$n)
}
)
title = 'Desiree'
data.SE = data.long.SE[data.long.SE$Genotype == title, ]
myggplot(data.SE, title, cnt, lncol = 2, lims, subdir = 'transcriptomics')
6.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
# X11();
# plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 19# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') my.pheatmap(df, mytitle, mycond, subdir = 'transcriptomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'transcriptomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'transcriptomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
openxlsx::write.xlsx(Desiree.stat, '../reports/Desiree.logFC-transcriptomics_leaves.xlsx', asTable = TRUE)6.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-transcriptomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'transcriptomics',
width = 6, height = 6, cnt = cnt)7 tubers metabolomics
fpi = file.path('..', 'input')
fn = "data_targeted.xlsx"
omics = openxlsx::read.xlsx(file.path(fpi, fn), sheet = 'metabolomics-tubers')
data.table::setDF(omics)
Genotype = 'Desiree'
omics = cbind(Genotype, omics)
PostInductionDay = as.numeric(stringr::str_split_i(gsub('S|P', '', omics$SampleID), '_', i = 2))
omics = cbind(PostInductionDay, omics)
Condition = gsub('_.*', '', omics$SampleID)
omics = cbind(Condition, omics)
omics$Condition = factor(omics$Condition, levels = c('C', 'H', 'D', 'HD', 'W'))
table(omics$Condition, omics$PostInductionDay)##
## 28
## C 12
## H 12
## D 12
## HD 12
## W 8mygroup = paste(omics$Condition, stringr::str_pad(omics$PostInductionDay, 1, pad = "0"), sep = '_')
# table(mygroup %in% comp$V1)
# table(mygroup %in% comp$V3)
omics = cbind(mygroup, omics)7.1 palette
my.ggplot.palette = c('#7F7F7F', '#C00000', '#FFC000', '#ED7D31',
# '#70AD47',
'#4472C4')
extend.palette = cbind(my.ggplot.palette, c('C', 'H', 'D', 'HD',
#'HDW',
'W'))
colnames(extend.palette) = c('colour', 'group')
extend.palette = merge(extend.palette, omics[, 1:4], by.x = 'group', by.y = 'Condition', all = TRUE)7.2 density
# column in which measurements start
nx = 9
data = omics
# colnames(data)[nx:ncol(data)]
data[, nx:ncol(data)] = apply(data[, nx:ncol(data)] , 2, as.numeric)
data.long = tidyr::gather(data, molecule, measurement, colnames(data)[nx]:colnames(data)[ncol(data)], factor_key=FALSE)
data.long$measurement = as.numeric(data.long$measurement)
data.long$molecule = factor(data.long$molecule, levels = colnames(data)[nx:ncol(data)])
data.long$PostInductionDay = ordered(data.long$PostInductionDay, levels = sort(unique(data.long$PostInductionDay )))
data.long = data.long[!is.na(data.long$measurement), ]
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = Condition, colour = Condition)) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = my.ggplot.palette) +
scale_fill_manual(name = 'Legend',
values = my.ggplot.palette) 
# X11()
ggplot(data.long[data.long$Genotype == 'Desiree', ], aes(log2(measurement), fill = mygroup , colour = mygroup )) +
geom_density(alpha = 0.1) +
ggtitle('Desiree') +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]) +
scale_fill_manual(name = 'Legend',
values = unique(extend.palette[, 2:3])[, 1]
) 
7.3 plot measurements
cd = sort(unique(data.long.SE$PostInductionDay))
mypallette = my.ggplot.palette
title = 'Tubers'
lncol = 2
subdir = 'tubers-metabolomics'
# X11();
myplotY = ggplot(data.long, aes(x = Condition ,
y = measurement,
group = Condition,
shape = Condition,
fill = Condition,
colour = Condition)) +
geom_boxplot() +
facet_wrap(~ molecule, ncol = ncol,
strip.position = "top",
scales = 'free_y',
drop = FALSE
) +
ggtitle(title) +
theme_classic() +
scale_colour_manual(name = 'Legend',
values = mypallette) +
scale_fill_manual(name = 'Legend',
values = mypallette
) +
labs(x = '',
y = 'Relative molecule abundance [+/- SE]') +
scale_y_continuous(# trans = log2_trans(),
labels = scales::comma
) +
theme(axis.text.x = element_text (angle = 0, # 45,
vjust = 1,
hjust = 1,
size = 12),
axis.text.y = element_text (angle = 0,
vjust = 1,
hjust = 1,
size = 12),
axis.title.x = element_text (angle = 0, # 45,
#vjust = 1,
#hjust = 1,
size = 12,
face = "bold"),
axis.title.y = element_text (angle = 90,
# vjust = 1,
# hjust = 1,
size = 12,
face = "bold"),
axis.text = element_text( angle = 90 ),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.position = "bottom",
legend.background = element_rect(fill=NULL,
size=0.1, linetype="solid"),
legend.title = element_text(color = NULL,
size = 12,
face = "bold"),
legend.text = element_text( size = 12),
strip.text = element_text(size = 12)) +
theme(
legend.box = "horizontal",
legend.key.width = unit(2, 'cm'),
legend.position = c(1, 0),
legend.justification = c(1, 0)) +
guides(colour = guide_legend(ncol = lncol)) +
guides(linetype = guide_legend(ncol = lncol)) +
guides(shape = "none") +
theme(panel.spacing.y = unit(1.5, "lines")) +
labs(color = "Legend", linetype = "Legend", shape = "Legend")
plot(myplotY) 
# dev.off()
fng = paste0(stringr::str_pad(cnt, 3, pad = "0"), '_', title, "_L.pdf")
ggsave(file.path('..', 'reports', subdir, fng),
width = 16, height = 12, units = "in")
fng = paste0(stringr::str_pad(cnt, 3, pad = "0"), '_', title, "_L.svg")
ggsave(file.path('..', 'reports', subdir, fng),
width = 16, height = 12, units = "in")7.4 correlation
mytitle = 'Desiree'
df = omics[omics$Genotype == mytitle, ]
df = as.matrix(df[, nx:ncol(df)])
df = apply(df, 2, as.numeric)
cs = colSums(is.na(df))/nrow(df)
if (any(cs == 1)) df = df[, -which(cs == 1)]
rownames(df) = omics$SampleID[omics$Genotype == mytitle]
mycond = levels(omics$Condition)
# X11();
# plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 27# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'C'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'H'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'D'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'W'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') my.pheatmap(df, mytitle, mycond, subdir = 'tubers-metabolomics')
mycond = 'HD'
df = omics[omics$Genotype == mytitle & omics$Condition == mycond, ]
df = as.matrix(df[, nx:ncol(df)])
# X11(); plot.new();
my.pairs.panels(df, mytitle, mycond, cccex = 1)
dev.copy(pdf,
file.path('..', 'reports', 'tubers-metabolomics', paste0('SPLOM_', 'PCC_', mytitle, '_', paste(mycond, collapse = '-'), ".pdf")),
width = 13, height = 13)## pdf
## 2# try(dev.off(dev.list()["RStudioGD"]),silent=TRUE); try(dev.off(),silent=TRUE);graphics.off()
my.heatmaply(df, mytitle, mycond, subdir = 'tubers-metabolomics') genotype = 'Desiree'
De.log2FC = NULL
De.p = NULL
comp = rbind(cbind('H_28', '-', 'C_28'),
cbind('D_28', '-', 'C_28'),
cbind('HD_28', '-', 'C_28'),
cbind('W_28', '-', 'C_28'))
for (i in 1:nrow(comp)) {
# print(i)
# print( as.character(comp[i, ]))
dtX = data.long[data.long$mygroup %in% comp[i, c(1,3)] & data.long$Genotype %in% genotype, ]
stress = dtX[dtX$mygroup == as.character(comp[i, 1]), ]
Ctrl = dtX[dtX$mygroup == as.character(comp[i, 3]), ]
StressC = gsub('_.*', '', comp[i, 1])
CtrlC = gsub('_.*', '', comp[i, 3])
log2FC = my.logFC(Ctrl = Ctrl, stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
p = my.customised.t.test(Ctrl = Ctrl, Stress = stress, StressC = StressC, CtrlC = CtrlC, genotype = genotype)
De.log2FC = rbind(De.log2FC, log2FC)
De.p = rbind(De.p, p)
}
Desiree.stat = merge(De.log2FC, De.p,
by.y = c("Genotype", "molecule", "group2", "group1"),
by.x = c("Genotype", "molecule", "stressGroup", "controlGroup"),
all = TRUE)
openxlsx::write.xlsx(Desiree.stat, '../reports/Desiree.logFC-metabolomics_tubers.xlsx', asTable = TRUE)7.5 heatmap
comp = as.data.frame(comp)
forHeatmap = comp[intersect(grep('C', comp[, 1], invert = TRUE), grep('C', comp[, 3])), ]
forHeatmap$comparison = paste(forHeatmap[, 1], forHeatmap[, 3], sep = '-')
df = Desiree.stat[Desiree.stat$stressGroup %in% forHeatmap[, 1] & Desiree.stat$controlGroup %in% forHeatmap[, 3], ]
my.pheatmap.logFC(df, forHeatmap,
main = 'Desiree-tubers-metabolomics',
cluster_rows = FALSE, cluster_cols = FALSE,
subdir = 'tubers-metabolomics',
width = 6, height = 6, cnt = cnt)## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.1 (2024-06-14 ucrt)
## os Windows 10 x64 (build 19045)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate English_United Kingdom.utf8
## ctype English_United Kingdom.utf8
## tz Europe/Ljubljana
## date 2024-11-05
## pandoc 3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.4.1)
## backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
## broom 1.0.7 2024-09-26 [1] CRAN (R 4.4.1)
## bslib 0.8.0 2024-07-29 [1] CRAN (R 4.4.1)
## ca 0.71.1 2020-01-24 [1] CRAN (R 4.4.1)
## cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.1)
## car 3.1-3 2024-09-27 [1] CRAN (R 4.4.1)
## carData 3.0-5 2022-01-06 [1] CRAN (R 4.4.1)
## cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1)
## codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.1)
## colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.1)
## corrplot * 0.95 2024-10-14 [1] CRAN (R 4.4.1)
## crosstalk 1.2.1 2023-11-23 [1] CRAN (R 4.4.1)
## data.table 1.16.2 2024-10-10 [1] CRAN (R 4.4.1)
## dendextend 1.18.1 2024-10-13 [1] CRAN (R 4.4.1)
## devtools 2.4.5 2022-10-11 [1] CRAN (R 4.4.1)
## digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
## dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.4.1)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.1)
## evaluate 1.0.1 2024-10-10 [1] CRAN (R 4.4.1)
## fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.1)
## farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.1)
## fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.4.1)
## Formula 1.2-5 2023-02-24 [1] CRAN (R 4.4.0)
## fs 1.6.5 2024-10-30 [1] CRAN (R 4.4.1)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.1)
## ggh4x * 0.2.8 2024-01-23 [1] CRAN (R 4.4.1)
## ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.1)
## glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.4.1)
## gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
## heatmaply * 1.5.0 2023-10-06 [1] CRAN (R 4.4.1)
## highr 0.11 2024-05-26 [1] CRAN (R 4.4.1)
## htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.1)
## htmlwidgets * 1.6.4 2023-12-06 [1] CRAN (R 4.4.1)
## httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.1)
## httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.1)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.4.1)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.4.1)
## jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.4.1)
## knitr 1.48 2024-07-07 [1] CRAN (R 4.4.1)
## labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.0)
## later 1.3.2 2023-12-06 [1] CRAN (R 4.4.1)
## lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.1)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.4.1)
## lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.1)
## magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.4.1)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.1)
## mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.1)
## mnormt 2.1.1 2022-09-26 [1] CRAN (R 4.4.0)
## munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.1)
## nlme 3.1-166 2024-08-14 [2] CRAN (R 4.4.1)
## openxlsx 4.2.7.1 2024-09-20 [1] CRAN (R 4.4.1)
## pheatmap 1.0.12 2019-01-04 [1] CRAN (R 4.4.1)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.1)
## pkgbuild 1.4.5 2024-10-28 [1] CRAN (R 4.4.1)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.1)
## pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.1)
## plotly * 4.10.4 2024-01-13 [1] CRAN (R 4.4.1)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.4.1)
## profvis 0.4.0 2024-09-20 [1] CRAN (R 4.4.1)
## promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.1)
## psych 2.4.6.26 2024-06-27 [1] CRAN (R 4.4.1)
## purrr 1.0.2 2023-08-10 [1] CRAN (R 4.4.1)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.1)
## ragg 1.3.3 2024-09-11 [1] CRAN (R 4.4.1)
## RColorBrewer * 1.1-3 2022-04-03 [1] CRAN (R 4.4.0)
## Rcpp 1.0.13-1 2024-11-02 [1] CRAN (R 4.4.1)
## registry 0.5-1 2019-03-05 [1] CRAN (R 4.4.0)
## remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.1)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.4.1)
## rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.1)
## rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.1)
## rstatix * 0.7.2 2023-02-01 [1] CRAN (R 4.4.1)
## rstudioapi 0.17.1 2024-10-22 [1] CRAN (R 4.4.1)
## sass 0.4.9 2024-03-15 [1] CRAN (R 4.4.1)
## scales * 1.3.0 2023-11-28 [1] CRAN (R 4.4.1)
## seriation 1.5.6 2024-08-19 [1] CRAN (R 4.4.1)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.1)
## shiny 1.9.1 2024-08-01 [1] CRAN (R 4.4.1)
## stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
## stringr 1.5.1 2023-11-14 [1] CRAN (R 4.4.1)
## svglite 2.1.3 2023-12-08 [1] CRAN (R 4.4.1)
## systemfonts 1.1.0 2024-05-15 [1] CRAN (R 4.4.1)
## textshaping 0.4.0 2024-05-24 [1] CRAN (R 4.4.1)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.1)
## tidyr 1.3.1 2024-01-24 [1] CRAN (R 4.4.1)
## tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.1)
## TSP 1.2-4 2023-04-04 [1] CRAN (R 4.4.1)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.1)
## usethis 3.0.0 2024-07-29 [1] CRAN (R 4.4.1)
## utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.1)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.1)
## viridis * 0.6.5 2024-01-29 [1] CRAN (R 4.4.1)
## viridisLite * 0.4.2 2023-05-02 [1] CRAN (R 4.4.1)
## webshot 0.5.5 2023-06-26 [1] CRAN (R 4.4.1)
## withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
## xfun 0.49 2024-10-31 [1] CRAN (R 4.4.1)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.1)
## yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.1)
## zip 2.3.1 2024-01-27 [1] CRAN (R 4.4.1)
##
## [1] C:/Users/majaz/AppData/Local/R/win-library/4.4
## [2] C:/Program Files/R/R-4.4.1/library
##
## ──────────────────────────────────────────────────────────────────────────────